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Filtering by: Creator O'Halloran, Thomas L. Remove constraint Creator: O'Halloran, Thomas L. Rights Statement In Copyright Remove constraint Rights Statement: In Copyright Subject Machine learning Remove constraint Subject: Machine learning

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  • Machine Learning Methods for Computational Sustainability

    Creator:
    Liu, Liping
    Abstract:
    Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
    Resource Type:
    Dissertation
    Full Text:
    . Then the complexity is O(KVNd). Since V is usually larger than L, the overall complexity of one EM

  • Learning cost-sensitive diagnostic policies from data

    Creator:
    Zubek, Valentina Bayer
    Abstract:
    In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body tem- perature, weight, etc.) and laboratory measurements (white blood count, CT scan,...
    Resource Type:
    Dissertation
    Full Text:
    . Title: Learning Cost-Sensitive Diagnostic Policies from Data. Abstract approved: Thomas G. Dietterich

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
    Full Text:
    : Improving Automated Email Tagging with Implicit Feedback Abstract approved: Thomas G. Dietterich Machine

  • A study of methods for fine-grained object classification of arthropod specimens

    Creator:
    Lin, Junyuan
    Abstract:
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
    Resource Type:
    Masters Thesis
    Full Text:
    Fine-grained Object Classification of Arthropod Specimens Abstract approved: Thomas G. Dietterich

  • Constraining Bayesian network learning with qualitative models

    Creator:
    Altendorf, Eric E.
    Abstract:
    Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
    Resource Type:
    Masters Thesis
    Full Text:
    Network Learning with Qualitative Models. Abstract approved: Thomas G. Dietterich Machine learning

  • Creating, Understanding and Applying Machine Learning Models of Multiple Species

    Creator:
    Griffioen, Arwen Twinkle E.
    Abstract:
    Many problems in ecology and conservation biology can be formulated and solved using machine learning algorithms for multi-label classification. This dissertation addresses three topics related to predicting the distributions of multiple species. It improves existing methods and proposes a new modeling paradigm to address the multi-species, multi-label problem. The first...
    Resource Type:
    Dissertation
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    Species Abstract approved: ______________________________________________________ Thomas G

  • Activity recognition in desktop environments

    Creator:
    Shen, Jianqiang
    Abstract:
    Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
    Resource Type:
    Dissertation
    Full Text:
    s s c h e d u l e m e e t i n g s o l v e a J I R A i s s u e p r e p a r i n g q u a r t e r

  • Applying machine learning for prediction, recommendation, and integration

    Creator:
    Bao, Xinlong
    Abstract:
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
    Resource Type:
    Dissertation
    Full Text:
    approved: Thomas G. Dietterich This dissertation explores the idea of applying machine learning

  • Learning greedy policies for the easy-first framework

    Creator:
    Xie, Jun
    Abstract:
    Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
    Resource Type:
    Masters Thesis
    Full Text:
    my thesis committee: Prof. Thomas G. Dietterich and Prof. Prasad Tadepalli, for their encouragement

  • Object detection in biological images using a search-based framework

    Creator:
    Lam, Michael
    Abstract:
    This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
    Resource Type:
    Masters Thesis
    Full Text:
    Framework Abstract approved: Sinisa Todorovic Thomas Dietterich This thesis addresses a basic problem